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#: ../source/paddlenlp.transformers.ernie_m.modeling.rst:2
msgid "modeling"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering:1
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification:1
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification:1
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel:1
msgid "基类：:class:`paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel`"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:1
msgid "The bare ERNIE-M Model transformer outputting raw hidden-states."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:3
msgid ""
"This model inherits from "
":class:`~paddlenlp.transformers.model_utils.PretrainedModel`. Refer to "
"the superclass documentation for the generic methods."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:6
msgid ""
"This model is also a Paddle `paddle.nn.Layer "
"<https://www.paddlepaddle.org.cn/documentation "
"/docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use "
"it as a regular Paddle Layer and refer to the Paddle documentation for "
"all matter related to general usage and behavior."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward
msgid "参数"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:10
msgid ""
"Vocabulary size of `inputs_ids` in `ErnieMModel`. Also is the vocab size "
"of token embedding matrix. Defines the number of different tokens that "
"can be represented by the `inputs_ids` passed when calling `ErnieMModel`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:13
msgid ""
"Dimensionality of the embedding layer, encoder layers and pooler layer. "
"Defaults to `768`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:15
msgid "Number of hidden layers in the Transformer encoder. Defaults to `12`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:17
msgid ""
"Number of attention heads for each attention layer in the Transformer "
"encoder. Defaults to `12`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:20
msgid ""
"Dimensionality of the feed-forward (ff) layer in the encoder. Input "
"tensors to ff layers are firstly projected from `hidden_size` to "
"`intermediate_size`, and then projected back to `hidden_size`. Typically "
"`intermediate_size` is larger than `hidden_size`. Defaults to `3072`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:25
msgid ""
"The non-linear activation function in the feed-forward layer. "
"``\"gelu\"``, ``\"relu\"`` and any other paddle supported activation "
"functions are supported. Defaults to `\"gelu\"`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:29
msgid ""
"The dropout probability for all fully connected layers in the embeddings "
"and encoder. Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:32
msgid ""
"The dropout probability used in MultiHeadAttention in all encoder layers "
"to drop some attention target. Defaults to `0.1`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:35
msgid ""
"The maximum value of the dimensionality of position encoding, which "
"dictates the maximum supported length of an input sequence. Defaults to "
"`512`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:38
msgid "The vocabulary size of the `token_type_ids`. Defaults to `2`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:41
msgid ""
"The standard deviation of the normal initializer for initializing all "
"weight matrices. Defaults to `0.02`.  .. note::     A normal_initializer "
"initializes weight matrices as normal distributions.     See "
":meth:`ErnieMPretrainedModel._init_weights()` for how weights are "
"initialized in `ErnieMModel`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:41
msgid ""
"The standard deviation of the normal initializer for initializing all "
"weight matrices. Defaults to `0.02`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:45
msgid ""
"A normal_initializer initializes weight matrices as normal distributions."
" See :meth:`ErnieMPretrainedModel._init_weights()` for how weights are "
"initialized in `ErnieMModel`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel:48
msgid "The index of padding token in the token vocabulary. Defaults to `1`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:1
msgid ""
"Indices of input sequence tokens in the vocabulary. They are numerical "
"representations of tokens that build the input sequence. It's data type "
"should be `int64` and has a shape of [batch_size, sequence_length]."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:5
msgid ""
"Indices of positions of each input sequence tokens in the position "
"embeddings. Selected in the range ``[0, max_position_embeddings - 1]``. "
"Shape as `[batch_size, num_tokens]` and dtype as int64. Defaults to "
"`None`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:9
msgid ""
"Mask used in multi-head attention to avoid performing attention on to "
"some unwanted positions, usually the paddings or the subsequent "
"positions. Its data type can be int, float and bool. When the data type "
"is bool, the `masked` tokens have `False` values and the others have "
"`True` values. When the data type is int, the `masked` tokens have `0` "
"values and the others have `1` values. When the data type is float, the "
"`masked` tokens have `-INF` values and the others have `0` values. It is "
"a tensor with shape broadcasted to `[batch_size, num_attention_heads, "
"sequence_length, sequence_length]`. For example, its shape can be  "
"[batch_size, sequence_length], [batch_size, sequence_length, "
"sequence_length], [batch_size, num_attention_heads, sequence_length, "
"sequence_length]. Defaults to `None`, which means nothing needed to be "
"prevented attention to."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward
msgid "返回"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:21
msgid ""
"Returns tuple (``sequence_output``, ``pooled_output``).  With the fields:"
"  - `sequence_output` (Tensor):     Sequence of hidden-states at the last"
" layer of the model.     It's data type should be float32 and its shape "
"is [batch_size, sequence_length, hidden_size].  - `pooled_output` "
"(Tensor):     The output of first token (`[CLS]`) in sequence.     We "
"\"pool\" the model by simply taking the hidden state corresponding to the"
" first token.     Its data type should be float32 and its shape is "
"[batch_size, hidden_size]."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:21
msgid "Returns tuple (``sequence_output``, ``pooled_output``)."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:12
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:23
msgid "With the fields:"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:27
msgid "`sequence_output` (Tensor):"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:26
msgid ""
"Sequence of hidden-states at the last layer of the model. It's data type "
"should be float32 and its shape is [batch_size, sequence_length, "
"hidden_size]."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:31
msgid "`pooled_output` (Tensor):"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:30
msgid ""
"The output of first token (`[CLS]`) in sequence. We \"pool\" the model by"
" simply taking the hidden state corresponding to the first token. Its "
"data type should be float32 and its shape is [batch_size, hidden_size]."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward
msgid "返回类型"
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:24
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward:15
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward:15
#: paddlenlp.transformers.ernie_m.modeling.ErnieMModel.forward:36
msgid "示例"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel:1
msgid "基类：:class:`paddlenlp.transformers.model_utils.PretrainedModel`"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel:1
msgid ""
"An abstract class for pretrained ERNIE-M models. It provides ERNIE-M "
"related `model_config_file`, `pretrained_init_configuration`, "
"`resource_files_names`, `pretrained_resource_files_map`, "
"`base_model_prefix` for downloading and loading pretrained models. Refer "
"to :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more "
"details."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMPretrainedModel.init_weights:1
msgid "Initialization hook"
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification:1
msgid ""
"Ernie-M Model with a linear layer on top of the output layer, designed "
"for sequence classification/regression tasks like GLUE tasks."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification:4
msgid "An instance of `paddlenlp.transformers.ErnieMModel`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification:6
msgid "The number of classes. Default to `2`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification:8
msgid ""
"The dropout probability for output of ERNIE-M. If None, use the same "
"value as `hidden_dropout_prob` of `paddlenlp.transformers.ErnieMModel` "
"instance. Defaults to `None`."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:1
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:3
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:5
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:7
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward:1
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward:3
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward:5
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward:7
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward:1
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward:3
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward:5
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward:7
msgid "See :class:`ErnieMModel`."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForSequenceClassification.forward:10
msgid ""
"Returns tensor `logits`, a tensor of the input text classification "
"logits. Shape as `[batch_size, num_classes]` and dtype as float32."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification:1
msgid ""
"ERNIE-M Model with a linear layer on top of the hidden-states output "
"layer, designed for token classification tasks like NER tasks."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering:5
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification:4
msgid "An instance of `ErnieMModel`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification:6
msgid "The number of classes. Defaults to `2`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification:8
msgid ""
"The dropout probability for output of ERNIE-M. If None, use the same "
"value as `hidden_dropout_prob` of `ErnieMModel` instance `ernie_m`. "
"Defaults to `None`."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForTokenClassification.forward:10
msgid ""
"Returns tensor `logits`, a tensor of the input token classification "
"logits. Shape as `[batch_size, sequence_length, num_classes]` and dtype "
"as `float32`."
msgstr ""

#: of paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering:1
msgid ""
"Ernie-M Model with a linear layer on top of the hidden-states output to "
"compute `span_start_logits` and `span_end_logits`, designed for question-"
"answering tasks like SQuAD."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:10
msgid ""
"Returns tuple (`start_logits`, `end_logits`).  With the fields:  - "
"`start_logits` (Tensor):     A tensor of the input token classification "
"logits, indicates the start position of the labelled span.     Its data "
"type should be float32 and its shape is [batch_size, sequence_length].  -"
" `end_logits` (Tensor):     A tensor of the input token classification "
"logits, indicates the end position of the labelled span.     Its data "
"type should be float32 and its shape is [batch_size, sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:10
msgid "Returns tuple (`start_logits`, `end_logits`)."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:16
msgid "`start_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:15
msgid ""
"A tensor of the input token classification logits, indicates the start "
"position of the labelled span. Its data type should be float32 and its "
"shape is [batch_size, sequence_length]."
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:19
msgid "`end_logits` (Tensor):"
msgstr ""

#: of
#: paddlenlp.transformers.ernie_m.modeling.ErnieMForQuestionAnswering.forward:19
msgid ""
"A tensor of the input token classification logits, indicates the end "
"position of the labelled span. Its data type should be float32 and its "
"shape is [batch_size, sequence_length]."
msgstr ""

